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    <article class="h-entry post-text"><header><h1 class="p-name entry-title"><a href="posts/slott-2015075-2015-06-30-report.html" class="u-url">双色球2015075期(2015-06-30)数据分析报告</a></h1>
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                Tao Junjie
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            <p class="dateline"><a href="posts/slott-2015075-2015-06-30-report.html" rel="bookmark"><time class="published dt-published" datetime="2015-07-01T08:00:00+08:00" title="2015-07-01 08:00">2015-07-01 08:00</time></a></p>
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<p>如有雷同，纯属巧合</p>
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    </article><article class="h-entry post-text"><header><h1 class="p-name entry-title"><a href="posts/dlott-15074-2015-06-29-report.html" class="u-url">大乐透15074期(2015-06-29)数据分析报告</a></h1>
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                Tao Junjie
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            <p class="dateline"><a href="posts/dlott-15074-2015-06-29-report.html" rel="bookmark"><time class="published dt-published" datetime="2015-06-30T08:00:00+08:00" title="2015-06-30 08:00">2015-06-30 08:00</time></a></p>
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<p>如有雷同，纯属巧合</p>
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    </article><article class="h-entry post-text"><header><h1 class="p-name entry-title"><a href="posts/4-from-linear-regression-to-logistic-regression.html" class="u-url">4-from-linear-regression-to-logistic-regression</a></h1>
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                Tao Junjie
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            <p class="dateline"><a href="posts/4-from-linear-regression-to-logistic-regression.html" rel="bookmark"><time class="published dt-published" datetime="2015-06-29T20:49:07+08:00" title="2015-06-29 20:49">2015-06-29 20:49</time></a></p>
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<h2 id="从线性回归到逻辑回归">从线性回归到逻辑回归<a class="anchor-link" href="posts/4-from-linear-regression-to-logistic-regression.html#%E4%BB%8E%E7%BA%BF%E6%80%A7%E5%9B%9E%E5%BD%92%E5%88%B0%E9%80%BB%E8%BE%91%E5%9B%9E%E5%BD%92">¶</a>
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<p>在第2章，线性回归里面，我们介绍了一元线性回归，多元线性回归和多项式回归。这些模型都是广义线性回归模型的具体形式，广义线性回归是一种灵活的框架，比普通线性回归要求更少的假设。这一章，我们讨论广义线性回归模型的具体形式的另一种形式，逻辑回归（logistic regression）。</p>
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    </article><article class="h-entry post-text"><header><h1 class="p-name entry-title"><a href="posts/6-clustering-with-k-means.html" class="u-url">6-clustering-with-k-means</a></h1>
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                Tao Junjie
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            <p class="dateline"><a href="posts/6-clustering-with-k-means.html" rel="bookmark"><time class="published dt-published" datetime="2015-06-29T20:29:49+08:00" title="2015-06-29 20:29">2015-06-29 20:29</time></a></p>
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<h2 id="K-Means聚类">K-Means聚类<a class="anchor-link" href="posts/6-clustering-with-k-means.html#K-Means%E8%81%9A%E7%B1%BB">¶</a>
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<p>前面几章我们介绍了监督学习，包括从带标签的数据中学习的回归和分类算法。本章，我们讨论无监督学习算法，聚类（clustering）。聚类是用于找出不带标签数据的相似性的算法。我们将介绍K-Means聚类思想，解决一个图像压缩问题，然后对算法的效果进行评估。最后，我们把聚类和分类算法组合起来，解决一个半监督学习问题。</p>
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    </article><article class="h-entry post-text"><header><h1 class="p-name entry-title"><a href="posts/5-nonlinear-classification-and-regression-with-decision-trees.html" class="u-url">5-nonlinear-classification-and-regression-with-decision-trees</a></h1>
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                Tao Junjie
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            <p class="dateline"><a href="posts/5-nonlinear-classification-and-regression-with-decision-trees.html" rel="bookmark"><time class="published dt-published" datetime="2015-06-29T20:29:22+08:00" title="2015-06-29 20:29">2015-06-29 20:29</time></a></p>
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<h2 id="决策树——非线性回归与分类">决策树——非线性回归与分类<a class="anchor-link" href="posts/5-nonlinear-classification-and-regression-with-decision-trees.html#%E5%86%B3%E7%AD%96%E6%A0%91%E2%80%94%E2%80%94%E9%9D%9E%E7%BA%BF%E6%80%A7%E5%9B%9E%E5%BD%92%E4%B8%8E%E5%88%86%E7%B1%BB">¶</a>
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<p>前面几章，我们介绍的模型都是广义线性模型，基本方法都是通过联接方程构建解释变量与若干响应变量的关联关系。我们用多元线性回归解决回归问题，逻辑回归解决分类问题。本章我们要讨论一种简单的非线性模型，用来解决回归与分类问题，称为决策树（decision tree）。首先，我们将用决策树做一个广告屏蔽器，可以将网页中的广告内容屏蔽掉。之后，我们介绍集成学习（lensemble learning）方法，通过将一系列学习方法集成使用，以取得更好的训练效果。</p>
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    </article><article class="h-entry post-text"><header><h1 class="p-name entry-title"><a href="posts/slott-2015074-2015-06-28-report.html" class="u-url">双色球2015074期(2015-06-28)数据分析报告</a></h1>
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                Tao Junjie
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            <p class="dateline"><a href="posts/slott-2015074-2015-06-28-report.html" rel="bookmark"><time class="published dt-published" datetime="2015-06-29T08:00:00+08:00" title="2015-06-29 08:00">2015-06-29 08:00</time></a></p>
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<p>如有雷同，纯属巧合</p>
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    </article><article class="h-entry post-text"><header><h1 class="p-name entry-title"><a href="posts/dlott-15073-2015-06-27-report.html" class="u-url">大乐透15073期(2015-06-27)数据分析报告</a></h1>
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                Tao Junjie
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            <p class="dateline"><a href="posts/dlott-15073-2015-06-27-report.html" rel="bookmark"><time class="published dt-published" datetime="2015-06-28T08:00:00+08:00" title="2015-06-28 08:00">2015-06-28 08:00</time></a></p>
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<p>如有雷同，纯属巧合</p>
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    </article><article class="h-entry post-text"><header><h1 class="p-name entry-title"><a href="posts/slott-2015073-2015-06-25-report.html" class="u-url">双色球2015073期(2015-06-25)数据分析报告</a></h1>
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                Tao Junjie
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            <p class="dateline"><a href="posts/slott-2015073-2015-06-25-report.html" rel="bookmark"><time class="published dt-published" datetime="2015-06-26T08:00:00+08:00" title="2015-06-26 08:00">2015-06-26 08:00</time></a></p>
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<p>如有雷同，纯属巧合</p>
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    </article><article class="h-entry post-text"><header><h1 class="p-name entry-title"><a href="posts/dlott-15072-2015-06-24-report.html" class="u-url">大乐透15072期(2015-06-24)数据分析报告</a></h1>
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                Tao Junjie
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            <p class="dateline"><a href="posts/dlott-15072-2015-06-24-report.html" rel="bookmark"><time class="published dt-published" datetime="2015-06-25T08:00:00+08:00" title="2015-06-25 08:00">2015-06-25 08:00</time></a></p>
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<p>如有雷同，纯属巧合</p>
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    </article><article class="h-entry post-text"><header><h1 class="p-name entry-title"><a href="posts/1-the-fundamentals-of-machine-learning.html" class="u-url">1-the-fundamentals-of-machine-learning</a></h1>
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                Tao Junjie
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            <p class="dateline"><a href="posts/1-the-fundamentals-of-machine-learning.html" rel="bookmark"><time class="published dt-published" datetime="2015-06-24T13:45:05+08:00" title="2015-06-24 13:45">2015-06-24 13:45</time></a></p>
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<h2 id="机器学习基础">机器学习基础<a class="anchor-link" href="posts/1-the-fundamentals-of-machine-learning.html#%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E5%9F%BA%E7%A1%80">¶</a>
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<p>本章我们简要介绍下机器学习（Machine Learning）的基本概念。主要介绍机器学习算法的应用，监督学习和无监督学习（supervised-unsupervised learning）的应用场景，训练和测试数据的用法，学习效果评估方式。最后，我们介绍scikit-learn及其安装方法。</p>
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